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<article id="content">
<header>
<h1 class="title">Module <code>silk.datasets.hpatches.hpatches_dataset</code></h1>
</header>
<section id="section-intro">
<p>Setting up the HPatches dataset.</p>
<p>This file contains a function to load in images from hpatches directory
on faircluster and a PyTorch Dataset class for the HPatches dataset.</p>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

&#34;&#34;&#34;
Setting up the HPatches dataset.

This file contains a function to load in images from hpatches directory
on faircluster and a PyTorch Dataset class for the HPatches dataset.
&#34;&#34;&#34;

import os
from pathlib import Path

import cv2
import numpy as np
import torch
from silk.cv.homography import resize_homography
from silk.models.superpoint_utils import load_image

HPATCHES_DEVFAIR_DIR = os.path.join(&#34;hpatches&#34;, &#34;01042022&#34;)


class HPatchesDataset(torch.utils.data.Dataset):
    &#34;&#34;&#34;
    The HPatches datset class.
    &#34;&#34;&#34;

    def __init__(
        self,
        hpatches_path,
        img_alteration=None,
        num_to_load=None,
        img_size=None,  # none will have original size, or can give a scale factor as a float
        ignore_folders=(),
        max_img_size=None,
        min_img_size=None,
        force_min_max_resizing=False,
        grayscale=True,
    ):
        &#34;&#34;&#34;
        Initialize the HPatches dataset from the hpatches_path (hpatches directory).
        &#34;&#34;&#34;

        def _load_hpatches(
            hpatches_path,
            img_alteration,
            num_to_load,
            ignore_folders,
        ):
            &#34;&#34;&#34;
            Load an image from hpatches and all of its homographies, plus the homography
            matrices linking the original image to the transformed images.

            Args:
                hpatches_path (str): the path to the directory containing the hpatches
                    dataset images
                img_alteration (optional str): i for illumination changes, v for change
                    in image perspective. Default is None for loading all (i and v) images.
                num_to_load (optional int): the number of image sets to load (will load
                    5 image pairs per image set because there are 5 pairs in each
                    directory of the hpatches dataset)

            Returns:
                files (dict): a dictionary containing the image paths, warped image paths,
                    and homographies connecting each pair of images
            &#34;&#34;&#34;
            hpatches_path = Path(hpatches_path)

            folder_paths = [x for x in hpatches_path.iterdir() if x.is_dir()]
            image_paths = []
            warped_image_paths = []
            homographies = []

            count = 0

            # go through each image directory and load in images
            for path in folder_paths:
                if img_alteration == &#34;i&#34; and path.stem[0] != &#34;i&#34;:
                    continue

                if img_alteration == &#34;v&#34; and path.stem[0] != &#34;v&#34;:
                    continue

                if os.path.basename(path) in ignore_folders:
                    continue

                # NUM_IMAGES is always 5, the number of pairs of images per hpatches directory
                NUM_IMAGES = 5
                file_ext = &#34;.ppm&#34;

                for i in range(2, 2 + NUM_IMAGES):
                    # the original image is always 1.ppm
                    image_paths.append(str(Path(path, &#34;1&#34; + file_ext)))

                    # the warped images are named 2 through 6.ppm
                    warped_image_paths.append(str(Path(path, str(i) + file_ext)))

                    # there is one homography matrix for each warped image
                    homographies.append(np.loadtxt(str(Path(path, &#34;H_1_&#34; + str(i)))))

                count += 1

                if num_to_load is not None:
                    if count &gt;= num_to_load:
                        break

            files = {
                &#34;image_paths&#34;: image_paths,
                &#34;warped_image_paths&#34;: warped_image_paths,
                &#34;homography&#34;: homographies,
            }

            return files

        ignore_folders = set(ignore_folders)
        files = _load_hpatches(
            hpatches_path,
            img_alteration,
            num_to_load,
            ignore_folders,
        )

        self.img_size = img_size
        self.force_min_max_resizing = force_min_max_resizing
        self.max_img_size = max_img_size
        self.min_img_size = min_img_size
        self.image_paths = files[&#34;image_paths&#34;]
        self.warped_image_paths = files[&#34;warped_image_paths&#34;]
        self.homographies = files[&#34;homography&#34;]
        self.grayscale = grayscale

    def __len__(self):
        &#34;&#34;&#34;
        Get the length of the dataset.
        &#34;&#34;&#34;
        return len(self.image_paths)

    def __getitem__(self, index):
        &#34;&#34;&#34;
        Get an item from the dataset. Note that one item in the
        dataset is one image PAIR, and the label is the homography.
        &#34;&#34;&#34;
        original_image_path = self.image_paths[index]
        warped_image_path = self.warped_image_paths[index]
        homography = torch.tensor(self.homographies[index])

        # resize images
        # TODO(Pierre): Avoid loading entire image just to get the shape (we re-loed it later)
        image = cv2.imread(original_image_path)
        wimage = cv2.imread(warped_image_path)

        original_height, original_width = image.shape[:2]

        assert (self.max_img_size is None) or (self.min_img_size is None)
        if self.max_img_size:
            if (
                original_height &gt; self.max_img_size[0]
                or original_width &gt; self.max_img_size[1]
                or self.force_min_max_resizing
            ):
                r = min(
                    self.max_img_size[0] / original_height,
                    self.max_img_size[1] / original_width,
                )
                original_height = int(original_height * r)
                original_width = int(original_width * r)
        elif self.min_img_size:
            if (
                original_height &lt; self.min_img_size[0]
                or original_width &lt; self.min_img_size[1]
                or self.force_min_max_resizing
            ):
                r = max(
                    self.min_img_size[0] / original_height,
                    self.min_img_size[1] / original_width,
                )
                original_height = int(original_height * r)
                original_width = int(original_width * r)

        # keep original image sizes, ensuring that height and width are divisible by 8
        if self.img_size is None:
            img_height = original_height + (8 - original_height % 8) % 8
            img_width = original_width + (8 - original_width % 8) % 8
        # if argument is a float, scale by the factor
        elif type(self.img_size) == float:
            img_height = int(original_height * self.img_size)
            img_width = int(original_width * self.img_size)
            img_height += (8 - img_height % 8) % 8
            img_width += (8 - img_width % 8) % 8
        elif len(self.img_size) == 2:
            img_height = self.img_size[0]
            img_width = self.img_size[1]
        else:
            raise ValueError(&#34;img_size must be of type None or Float&#34;)

        original_image = load_image(
            original_image_path, img_height, img_width, as_gray=self.grayscale
        )
        warped_image = load_image(
            warped_image_path, img_height, img_width, as_gray=self.grayscale
        )

        homography = resize_homography(
            homography,
            image.shape,
            (img_height, img_width),
            wimage.shape,
        )

        return original_image, warped_image, homography</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="silk.datasets.hpatches.hpatches_dataset.HPatchesDataset"><code class="flex name class">
<span>class <span class="ident">HPatchesDataset</span></span>
<span>(</span><span>hpatches_path, img_alteration=None, num_to_load=None, img_size=None, ignore_folders=(), max_img_size=None, min_img_size=None, force_min_max_resizing=False, grayscale=True)</span>
</code></dt>
<dd>
<div class="desc"><p>The HPatches datset class.</p>
<p>Initialize the HPatches dataset from the hpatches_path (hpatches directory).</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class HPatchesDataset(torch.utils.data.Dataset):
    &#34;&#34;&#34;
    The HPatches datset class.
    &#34;&#34;&#34;

    def __init__(
        self,
        hpatches_path,
        img_alteration=None,
        num_to_load=None,
        img_size=None,  # none will have original size, or can give a scale factor as a float
        ignore_folders=(),
        max_img_size=None,
        min_img_size=None,
        force_min_max_resizing=False,
        grayscale=True,
    ):
        &#34;&#34;&#34;
        Initialize the HPatches dataset from the hpatches_path (hpatches directory).
        &#34;&#34;&#34;

        def _load_hpatches(
            hpatches_path,
            img_alteration,
            num_to_load,
            ignore_folders,
        ):
            &#34;&#34;&#34;
            Load an image from hpatches and all of its homographies, plus the homography
            matrices linking the original image to the transformed images.

            Args:
                hpatches_path (str): the path to the directory containing the hpatches
                    dataset images
                img_alteration (optional str): i for illumination changes, v for change
                    in image perspective. Default is None for loading all (i and v) images.
                num_to_load (optional int): the number of image sets to load (will load
                    5 image pairs per image set because there are 5 pairs in each
                    directory of the hpatches dataset)

            Returns:
                files (dict): a dictionary containing the image paths, warped image paths,
                    and homographies connecting each pair of images
            &#34;&#34;&#34;
            hpatches_path = Path(hpatches_path)

            folder_paths = [x for x in hpatches_path.iterdir() if x.is_dir()]
            image_paths = []
            warped_image_paths = []
            homographies = []

            count = 0

            # go through each image directory and load in images
            for path in folder_paths:
                if img_alteration == &#34;i&#34; and path.stem[0] != &#34;i&#34;:
                    continue

                if img_alteration == &#34;v&#34; and path.stem[0] != &#34;v&#34;:
                    continue

                if os.path.basename(path) in ignore_folders:
                    continue

                # NUM_IMAGES is always 5, the number of pairs of images per hpatches directory
                NUM_IMAGES = 5
                file_ext = &#34;.ppm&#34;

                for i in range(2, 2 + NUM_IMAGES):
                    # the original image is always 1.ppm
                    image_paths.append(str(Path(path, &#34;1&#34; + file_ext)))

                    # the warped images are named 2 through 6.ppm
                    warped_image_paths.append(str(Path(path, str(i) + file_ext)))

                    # there is one homography matrix for each warped image
                    homographies.append(np.loadtxt(str(Path(path, &#34;H_1_&#34; + str(i)))))

                count += 1

                if num_to_load is not None:
                    if count &gt;= num_to_load:
                        break

            files = {
                &#34;image_paths&#34;: image_paths,
                &#34;warped_image_paths&#34;: warped_image_paths,
                &#34;homography&#34;: homographies,
            }

            return files

        ignore_folders = set(ignore_folders)
        files = _load_hpatches(
            hpatches_path,
            img_alteration,
            num_to_load,
            ignore_folders,
        )

        self.img_size = img_size
        self.force_min_max_resizing = force_min_max_resizing
        self.max_img_size = max_img_size
        self.min_img_size = min_img_size
        self.image_paths = files[&#34;image_paths&#34;]
        self.warped_image_paths = files[&#34;warped_image_paths&#34;]
        self.homographies = files[&#34;homography&#34;]
        self.grayscale = grayscale

    def __len__(self):
        &#34;&#34;&#34;
        Get the length of the dataset.
        &#34;&#34;&#34;
        return len(self.image_paths)

    def __getitem__(self, index):
        &#34;&#34;&#34;
        Get an item from the dataset. Note that one item in the
        dataset is one image PAIR, and the label is the homography.
        &#34;&#34;&#34;
        original_image_path = self.image_paths[index]
        warped_image_path = self.warped_image_paths[index]
        homography = torch.tensor(self.homographies[index])

        # resize images
        # TODO(Pierre): Avoid loading entire image just to get the shape (we re-loed it later)
        image = cv2.imread(original_image_path)
        wimage = cv2.imread(warped_image_path)

        original_height, original_width = image.shape[:2]

        assert (self.max_img_size is None) or (self.min_img_size is None)
        if self.max_img_size:
            if (
                original_height &gt; self.max_img_size[0]
                or original_width &gt; self.max_img_size[1]
                or self.force_min_max_resizing
            ):
                r = min(
                    self.max_img_size[0] / original_height,
                    self.max_img_size[1] / original_width,
                )
                original_height = int(original_height * r)
                original_width = int(original_width * r)
        elif self.min_img_size:
            if (
                original_height &lt; self.min_img_size[0]
                or original_width &lt; self.min_img_size[1]
                or self.force_min_max_resizing
            ):
                r = max(
                    self.min_img_size[0] / original_height,
                    self.min_img_size[1] / original_width,
                )
                original_height = int(original_height * r)
                original_width = int(original_width * r)

        # keep original image sizes, ensuring that height and width are divisible by 8
        if self.img_size is None:
            img_height = original_height + (8 - original_height % 8) % 8
            img_width = original_width + (8 - original_width % 8) % 8
        # if argument is a float, scale by the factor
        elif type(self.img_size) == float:
            img_height = int(original_height * self.img_size)
            img_width = int(original_width * self.img_size)
            img_height += (8 - img_height % 8) % 8
            img_width += (8 - img_width % 8) % 8
        elif len(self.img_size) == 2:
            img_height = self.img_size[0]
            img_width = self.img_size[1]
        else:
            raise ValueError(&#34;img_size must be of type None or Float&#34;)

        original_image = load_image(
            original_image_path, img_height, img_width, as_gray=self.grayscale
        )
        warped_image = load_image(
            warped_image_path, img_height, img_width, as_gray=self.grayscale
        )

        homography = resize_homography(
            homography,
            image.shape,
            (img_height, img_width),
            wimage.shape,
        )

        return original_image, warped_image, homography</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.utils.data.dataset.Dataset</li>
<li>typing.Generic</li>
</ul>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="silk.datasets.hpatches" href="index.html">silk.datasets.hpatches</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="silk.datasets.hpatches.hpatches_dataset.HPatchesDataset" href="#silk.datasets.hpatches.hpatches_dataset.HPatchesDataset">HPatchesDataset</a></code></h4>
</li>
</ul>
</li>
</ul>
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